Low-discrepancy Sampling for Full Reference Image Quality Assessment Speed Up

Abstract

Image quality assessment (IQA) aims to predict the image quality perceived by the human visual system (HVS). Full Reference (FR) image quality assessment is an objective algorithm requiring information about the reference image for quality assessment. Consequently, the FR algorithms may need a high number of operations to complete the evaluation. Another relevant point is that IQA based on Convolutional Neural Networks (CNN) requires a long training. Considering the high computational cost of FR assessment, we propose to use sampling methods as an alternative to the conventional IQA. First, we apply Van der Corput-Halton, Sobol, and uniform sampling methods to obtain a small representation of the images. Afterwards, we evaluate the sampled image using the Peak Signal-to-Noise Ratio (PSNR), Structural Similarity (SSIM), and Deep Image Quality Measure for FR (DIQaM-FR) metrics. The experimental results reveal that 7.8% of image pixels of the Live database are sufficient to obtain approximate values of SSIM and low mean error of PSNR. The sampling blocks used in the training of DIQaM-FR demonstrate to be adequate for training the model showing a correlation of 0.968 for SROCC applied on the Live database and a considerably lower training time.